As discussed, retrieval-based models may give informative but inappropriate responses while generation-based models often do the opposite. It is desirable to combine the best of both worlds. Early work [1]} attempts to re-rank the output from both models. For a deep integration, song2016two and yang2019hybrid extend the standard Seq2Seq encoder-decoder model [2]} with an extra encoder for encoding the retrieval result. The output of the extra encoder, along with the output from the original encoder for dialogue history, is used to feed the decoder. weston-etal-2018-retrieve use a single encoder that takes the concatenation of the original dialogue history and the retrieved as input. wu2019response note that the retrieved information should be used in awareness of the context difference, and further proposed to construct an edit vector by explicitly encoding the lexical differences between the input dialogue history and the retrieved dialogue history. pandey2018exemplar further propose to weight different training instances by context similarity.
